In a diagnosis in which a medical image inspection apparatus represented by an X-ray computed tomography (X-ray CT) apparatus, a magnetic resonance imaging (MRI) apparatus or the like is used, it is common that a captured three-dimensional medical image (hereinafter also referred to as “volume data”) is reconstructed as a continuous two-dimensional image and then image interpretation is performed.
There is a trend of imaging apparatuses becoming more sophisticated every year and the data size per volume data tending to increase. In addition, especially in a CT apparatus, imaging of high-quality volume data using low doses has become possible and imaging opportunities have also tended to increase. Therefore, a burden on a doctor or an engineer who performs the image interpretation for this large amount of medical volume data is very high.
In order to mitigate the burden, there is growing a need for the use of computer aided detection, or computer aided diagnosis (CAD). CAD refers to a system and a technique which perform quantification and analysis of image information with a computer and an information processing technique based on a computer.
A typical function of CAD includes a function which automatically extracts a high suspicion disease region using an image processing technology from values and a distribution of voxels of the medical volume data as target data and then provides the high suspicion disease region as an interest region, for example. However, CAD performs only support regarding the diagnosis and thus confirmation of a doctor is required upon the diagnosis including determination of whether or not the interest region falls on a disease region.
In a case of performing the image interpretation of the volume data in which the interest region is set in advance by the CAD or the like, as matters required of a doctor or an engineer to confirm, there are confirmation whether or not the interest region is correctly set to the disease region while looking at the interest region set in advance and confirmation whether or not there is no disease region while looking at the area where the interest region has not been set. It is necessary to confirm the regions without oversights, as fast as possible.
In the related art, when performing the image interpretation of a large amount of volume data in which the interest region is set in advance, a technique of performing the interpretation without oversights, as fast as possible has been proposed.
For example, in PTL 1, an apparatus and a program which allow in-depth image interpretation by delaying a display speed of the image data generated in an image section intersecting with the interest region than a display speed of the image data generated in other image sections are proposed.